Whose story is your data telling? Top 10 reads on gender-disaggregated data

Gender-disaggregated data is essential for advancing gender equality and inclusivity in agri-food systems. It plays a critical role in shaping policies and practices that address the needs of both women and men. Yet, how often have we heard the all-too-convenient excuse, “there’s no data”—which is less about the data and more about dodging accountability for the gender gaps that impact households, communities and nations.

Gender-disaggregated data is not just about sorting women and men into categories; it is about capturing the complex realities shaped by local sociocultural norms—like who does what, who owns what, who makes decisions, and so on. In other words, it’s the “who, what and why” that policymakers and practitioners can’t afford to ignore.

Such data is not just a “nice-to-have”; rather, a must-have for designing, implementing and advancing gender equality in research, policy and practice.

Tracing its roots, the push for gender-disaggregated data emerged during significant global events such as the World Conference on Human Rights, Vienna, 1993 and the Fourth World Conference on Women, Beijing, 1995. These events pioneered the discourse of collecting gender-specific data to inform global policies. However, despite its recognized value, significant gaps persist in the global collection of gender-disaggregated data. Many countries lack comprehensive national statistics that adequately capture gender differences, particularly regarding climate change impacts.

These top-10 reads unpack the what, why and how of gender-disaggregated data. This knowledge is crucial for a wide range of actors, including researchers, practitioners, policymakers and funders within and related to agri-food systems who can start using gender data as a real game-changer. This curated list provides key lessons essential for advocating for consistent, actionable gender data, moving beyond its use in “ticking evaluation boxes”.

#1

The Gender Data Outlook 2024: Unlocking Capacity, Driving Change offers a fresh perspective on the variable gender-data capacities of 83 countries, emphasizing the need not only to produce gender-disaggregated data but also to transform it into actionable insights for meaningful change.

The outlook’s index evaluates three key dimensions: enabling environment, data production, and accessibility; but acknowledges the lack of comparable measures for the fourth dimension of data use.

This report highlights the critical role and use of regular user–producer dialogues, coordination between national statistical offices and national statistical systems, and consistent investment and budget allocations for collecting and analyzing gender data. According to the index, top-performing countries exhibit these practices, alongside targeted use of gender data in areas such as violence against women and unpaid care work.

This resource inspires one to think about the inevitable role of robust gender data for building inclusive and gender-equal futures within agri-food systems and beyond.

#2

Gender Statistics Training Curriculum developed by UN Women and the Statistical Institute for Asia and the Pacific (SIAP) https://data.unwomen.org/resources/gender-statistics-training-curriculum

This Gender Statistics Training Curriculum is essential reading for anyone who wants to understand gender data and its applications.

The curriculum introduces what comprises gender data, clarifies the distinction between sex-disaggregated and gender-disaggregated data, gives a comprehensive overview of gender indicators, and emphasizes the key principles for the proper production and use of data from a gender perspective. It includes methodological details for computing selected gender indicators within the global SDG framework and highlights the importance of aligning user needs with data supply to maximize its utility.

It is a resource for all levels of learners as it provides introductory modules on gender indicators for the beginners and  advanced lessons on techniques like regression analysis to explore multidimensional gender inequalities for professionals. The curriculum also offers guidance on effective communication strategies and creating appropriate gender-data products while highlighting the vital role of gender statistics in policymaking. At last, it explores the potential, characteristics and applications of big data as a source for producing gender statistics.

This open access, self-paced e-learning course is an invaluable resource for anyone seeking to learn about or apply gender data in their work.

#3

This discussion paper—Using big data for insights into the gender digital divide for girls—highlights the critical need for gender-disaggregated data to help address the digital divide impacting girls and women across sectors. For instance, globally, over 50 percent of women remain offline; and women are also 20 percent less likely to own smartphones, often relying on shared or borrowed devices, which limits their access to advanced digital services.

The paper explores innovative solutions, such as leveraging big data from platforms like Facebook and Google to generate disaggregated data insights based on sex, age, location and behaviour (such as interests) to measure global gender gaps in internet access (confirming traditional survey results). The paper stresses the urgent need for comprehensive gender-disaggregated data on women’s and girls’ digital usage to develop effective strategies for closing the digital gender divide.

It concludes that without addressing digital access and usage gaps, women cannot fully benefit from or adopt digital solutions, hindering their empowerment and broader systemic progress. This remains true for agri-food systems, as well as other sectors, where digital technologies can prove to be instrumental in bringing about transformative change.

#4

This book addresses the critical issue of gender-data gaps and their far-reaching consequences in our daily lives: Invisible Women: Data Bias in a World Designed for Men.

Studies on gender equality can be very complex, but Caroline Perez summarizes the data biases in a very relevant and easy to read way. Perez highlights how women are often under-represented in data collection, leading to biases in the design of products and policies that fail to consider women’s needs and experiences across crucial sectors such as healthcare, urban planning, and technology development. For example, medical research has historically focused on male subjects, leading to treatments that may not work effectively for women. Dietary guidelines are frequently based on male bodies and metabolism, which can result in recommendations that do not suit women’s nutritional needs. Conventional caloric intake recommendations may overlook women’s different energy requirements due to factors like pregnancy or lactation.

The book mentions that women play a crucial role in food production and nutrition, particularly in developing countries. However, their contributions are often unrecognized in agricultural-data collection, leading to policies that fail to support women’s roles in food security and nutrition. It also discusses the economic implications of ignoring women’s health and nutritional needs which, if taken care of, can significantly contribute to national GDPs. Poor health outcomes for women can lead to increased healthcare costs and lost productivity, affecting broader economic growth.

This book is important for understanding how gender data gaps persist and impact our daily lives, with real-world examples illustrating the social and economic consequences. It also offers insights into what can be done to address these gaps. Invisible Women serves as a rallying cry for improved data practices and institutional policies that incorporate women’s perspectives.

#5

Published in 2010, this article on the State of Sex-disaggregated Data for Assessing the Impact of Climate Change. Procedia Environmental Sciences remains as relevant today as it was 15 years ago.

It examines gender-disaggregated data, particularly in the context of climate change, and offers critical lessons to improve policy formulation and implementation. To address the needs of women and men differently affected by climate change and socioeconomic factors, gender-disaggregated data is essential for evidence-based policymaking. It enables an understanding of the unique vulnerabilities and capacities of different gender groups, which is crucial for designing targeted interventions and allocating resources effectively.

The article provides actionable recommendations to bridge the gaps in collecting gender-disaggregated data, including the timely collection of gender-specific data following climate-related events to understand distinct vulnerabilities; training national statistical officers to effectively gather and analyze gender-disaggregated data; and enacting legislative support to ensure the continuous production of gender statistics, even during crises. It suggests that tools such as the Gender Vulnerability Index, which encapsulate multiple stressors affecting different genders, can offer a more nuanced understanding of their respective vulnerabilities.

This is an essential read, emphasizing that without such data, the challenges and needs of vulnerable populations, especially women, remain invisible. By “connecting the dots” between gender equality, economic growth, and social justice, its argument serves as a call to action for people who collect and use the data to improve how they collect and use gender-disaggregated data, while developing strategies to create a cohesive data ecosystem that supports gender equality.

#6

Gender- and youth-sensitive data collection tools to support decision making for inclusive sustainable agricultural intensification: this useful paper highlights the essential role of gender-disaggregated data in achieving equitable sustainable agricultural intensification. It focuses on the need to understand the unequal access and control over agricultural resources among women and youth, which influences the socioeconomic (and other) outcomes of agricultural intensification for them.

Critiquing the existing tools, it notes that large-scale surveys often fail to provide timely results for decision-making and are too time consuming to conduct. It introduces multiple low-cost, participatory alternatives like activity profiling, daily time use, drudgery score, participatory mapping and leaky bucket exercise for detecting gender and youth inequities early on.

Based on the information collected from the testing and adaptation of these tools, it establishes the significance of such methodologies in enabling informed decision-making by promoting participatory engagement and meeting decision-makers’ needs to effectively assess gender and age disparities in sustainable agricultural intensification interventions. It thus provides one way for the policymakers to effectively implement gender-transformative approaches.

#7

This article, Gender Data Gaps in Agriculture and Land Ownership: Uncovering the Blind Side of Policymaking, comprehensively analyzes the gender-data gaps in agriculture, with a specific focus on women’s land ownership in India.

Despite women comprising a significant portion of the agricultural workforce (around 43 percent globally and 50 percent of farm labor), they only own about 15 percent of agricultural land. This disparity highlights the need for accurate, gender-disaggregated data to address the persistent inequalities. The authors argue that the lack of such data leads to ineffective policies that fail to account for the contributions and needs of women farmers. Without reliable data on women’s land ownership, policymakers often struggle to design programs that support women’s access to resources, credit, and government schemes, perpetuating their marginalization.

Many women farmers often remain invisible in datasets, which excludes them from economic discussions and policy decisions. Such invisibility translates into fewer and lesser opportunities for improving food security and economic empowerment.

Interestingly, authors also explore the role of personal laws—such as Hindu and Muslim inheritance laws in India—that complicate women’s land rights and exacerbate gender disparities. The lack of data on women’s land ownership means these legal complexities are often overlooked in policymaking.

The article links gender-disaggregated data to global commitments for achieving gender equality under the SDGs, stressing that without accurate data, it is impossible to accurately track progress or design effective policies. For instance, collecting data on unpaid labor contributions would shed light on the economic value women add through caregiving roles, often ignored in traditional economic assessments.

The article also discusses examples such as the Mewar Angithi (a stove design that can reduce household pollution) and the under-reporting of female farmers’ suicides in India, highlighting the critical need for gender-specific data to address the unique challenges women face. The article’s findings are relevant not only for India but also for global audiences, as they emphasize the importance of gender-disaggregated data in shaping more inclusive policies in agriculture and development.

#8

In a world shaped by global challenges and technological advancements, the principles of data feminism offer a critical lens to tackle issues like climate change, public health crises, and economic disparities. This book, Data Feminism, delves into the intersection of data science and feminist theory, highlighting the transformative potential of gender-disaggregated data in addressing social inequalities.

Using a narrative style and integrating intersectionality—acknowledging the interplay of gender with race, class and sexuality—it aims to show how such data can amplify the visibility (or invisibility) of marginalized groups, challenge (or reinforce) stereotypes, and drive (or obstruct) meaningful social change.

The book advocates for data literacy so that individuals and organizations can critically engage with data, and explores the role of big data and machine learning in improving analyses of equity.

Data feminism emerges as a tool for social justice, strengthening advocacy and shaping policies through evidence-based insights. It recognizes the human stories behind the numbers, from those whose experiences are counted to those who remain invisible. Acknowledging the limitations of data, it calls for ongoing critical reflection and action to challenge entrenched systems of discrimination.

This essential read provides a framework for reimagining the role of data in shaping a more equitable world, rooted in intersectional feminist thought and a commitment to transformative change. It asks the urgent question: How can we use data to remake the world?

#9

This book chapter, Gender-Disaggregated Data in Agriculture, focuses on how inadequate data collection methodologies create significant gaps in understanding women’s roles in India’s rural economy.

It emphasizes the need for comprehensive, high-quality gender-disaggregated data to assess women’s empowerment, particularly in areas like land ownership, workforce participation, and agricultural income sharing. Current surveys often fail to capture the full extent of women’s contributions and ownership in rural settings, with issues like the ambiguous definition of “head of household” obscuring women’s decision-making power, and labor force surveys overlooking informal and unpaid work predominantly performed by women.

The chapter highlights the importance of broadening definitions to include both paid and unpaid work, and it suggests conducting pilot surveys that focus specifically on rural household incomes with gender-disaggregated information. It also recommends supplementing existing labor-force surveys with time-use surveys to better capture women’s work.

The piece stresses the need for making gender-disaggregated data publicly available to support research, inform policymaking, and improve women’s societal status. Readers can expect a detailed exploration of the critical need for improved data collection methodologies to accurately reflect women’s roles in the rural economy in India and other agrarian contexts.

#10

This Scoping review on gender-disaggregated data in climate-smart agriculture assesses the availability of gender-disaggregated data in climate-smart agriculture (CSA) programming, identifies gaps, and proposes solutions to reduce gender inequalities across agri-food systems.

Critical gaps include a lack of commitment to gender equality, absence of clear indicators for impact measurement, and insufficient resources for long-term gender-focused outcomes. However, there are some positive trends emerging: some organizations integrate gender considerations across all project phases, tools are being used to assess gender impacts, and digital data collection is helping increase efficiency and accuracy.

Based on a global assessment and persistent gaps, this review recommends establishing an alliance to strengthen leadership in integrating gender equality across CSA programming. It stresses the need for: cost-efficient impact-measurement protocols, extended time frames for data-collection to track long-term outcomes, adequate funding, clear metrics, and targeted capacity building. For capturing nuanced data, it recommends better guidance on incorporating climate risk and gender analysis into decision-making.

These steps can help in realizing the transformative potential of CSA programs and ensuring that future CSA initiatives are more inclusive, equitable and responsive to people’s diverse needs.

 

In conclusion, these top-10 reads highlight the critical, yet often overlooked, role of gender-disaggregated data. While there has been steady progress over the last two decades, we are still far from cracking the code on how to effectively produce, use, analyze and integrate gender data—not just in agri-food systems, but across interconnected areas like health, climate change, migration and education.

Most importantly, in an age where data drives decisions, it’s crucial to ask, “Whose story is our data telling?”